TY - GEN
T1 - Autoencoder-Aided Visualization of Collections of Morse Complexes
AU - Li, Jixian
AU - Van Boxel, Daniel
AU - Levine, Joshua A.
N1 - Funding Information:
We thank the anonymous reviewers for their valuable opinions and comments. This work is supported in part by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under Award Number(s) DE-SC-0019039.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Though analyzing a single scalar field using Morse complexes is well studied, there are few techniques for visualizing a collection of Morse complexes. We focus on analyses that are enabled by looking at a Morse complex as an embedded domain decomposition. Specifically, we target 2D scalar fields, and we encode the Morse complex through binary images of the boundaries of decomposition. Then we use image-based autoencoders to create a feature space for the Morse complexes. We apply additional dimensionality reduction methods to construct a scatterplot as a visual interface of the feature space. This allows us to investigate individual Morse complexes, as they relate to the collection, through interaction with the scatterplot. We demonstrate our approach using a synthetic data set, microscopy images, and time-varying vorticity magnitude fields of flow. Through these, we show that our method can produce insights about structures within the collection of Morse complexes.
AB - Though analyzing a single scalar field using Morse complexes is well studied, there are few techniques for visualizing a collection of Morse complexes. We focus on analyses that are enabled by looking at a Morse complex as an embedded domain decomposition. Specifically, we target 2D scalar fields, and we encode the Morse complex through binary images of the boundaries of decomposition. Then we use image-based autoencoders to create a feature space for the Morse complexes. We apply additional dimensionality reduction methods to construct a scatterplot as a visual interface of the feature space. This allows us to investigate individual Morse complexes, as they relate to the collection, through interaction with the scatterplot. We demonstrate our approach using a synthetic data set, microscopy images, and time-varying vorticity magnitude fields of flow. Through these, we show that our method can produce insights about structures within the collection of Morse complexes.
KW - Autoencoders
KW - Dimensionality reduction
KW - Morse complex
UR - http://www.scopus.com/inward/record.url?scp=85145770287&partnerID=8YFLogxK
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U2 - 10.1109/TopoInVis57755.2022.00009
DO - 10.1109/TopoInVis57755.2022.00009
M3 - Conference contribution
AN - SCOPUS:85145770287
T3 - Proceedings - 2022 IEEE Workshop on Topological Data Analysis and Visualization, TopoInVis 2022
SP - 18
EP - 28
BT - Proceedings - 2022 IEEE Workshop on Topological Data Analysis and Visualization, TopoInVis 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Workshop on Topological Data Analysis and Visualization, TopoInVis 2022
Y2 - 17 October 2022
ER -